Sensing and Control for Robust Grasping with Simple Hardware

Robots can move, see, and navigate in the real world outside carefully structured factories, but they cannot yet grasp and manipulate objects without human intervention. Two key barriers are the complexity of current approaches, which require complicated hardware or precise perception to function effectively, and the challenge of understanding system performance in a tractable manner given the wide range of factors that impact successful grasping. This thesis presents sensors and simple control algorithms that relax the requirements on robot hardware, and a framework to understand the capabilities and limitations of grasping systems.Engineering and Applied Science

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Prevalence of haptic feedback in robot-mediated surgery : a systematic review of literature

© 2017 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Robotic Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11701-017-0763-4With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.Peer reviewe

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

Bio-inspired soft robotic systems: Exploiting environmental interactions using embodied mechanics and sensory coordination

Despite the widespread development of highly intelligent robotic systems exhibiting great precision, reliability, and dexterity, robots remain incapable of performing basic manipulation tasks that humans take for granted. Manipulation in unstructured environments continues to be acknowledged as a significant challenge. Soft robotics, the use of less rigid materials in robots, has been proposed as one means of addressing these limitations. The technique enables more compliant interactions with the environment, allowing for increasingly adaptive behaviours better suited to more human-centric applications. Embodied intelligence is a biologically inspired concept in which intelligence is a function of the entire system, not only the controller or `brain'. This thesis focuses on the use of embodied intelligence for the development of soft robots, with a particular focus on how it can aid both perception and adaptability. Two main hypotheses are raised: first, that the mechanical design and fabrication of soft-rigid hybrid robots can enable increasingly environmentally adaptive behaviours, and second, that sensing materials and morphology can provide intelligence that assists perception through embodiment. A number of approaches and frameworks for the design and development of embodied systems are presented that address these hypotheses. It is shown how embodiment in soft sensor morphology can be used to perform localised processing and thereby distribute the intelligence over the body of a system. Specifically in soft robots, sensor morphology utilises the directional deformations created by interactions with the environment to aid in perception. Building on and formalising these ideas, a number of morphology-based frameworks are proposed for detecting different stimuli. The multifaceted role of materials in soft robots is demonstrated through the development of materials capable of both sensing and changes in material property. Such materials provide additional functionality beyond their integral scaffolding and static mechanical characteristics. In particular, an integrated material has been created exhibiting both sensing capabilities and also variable stiffness and `tack’ force, thereby enabling complex single-point grasping. To maximise the intelligence that can be gained through embodiment, a design approach to soft robots, `soft-rigid hybrid' design is introduced. This approach exploits passive behaviours and body dynamics to provide environmentally adaptive behaviours and sensing. It is leveraged by multi-material 3D printing techniques and novel approaches and frameworks for designing mechanical structures. The findings in this thesis demonstrate that an embodied approach to soft robotics provides capabilities and behaviours that are not currently otherwise achievable. Utilising the concept of `embodiment' results in softer robots with an embodied intelligence that aids perception and adaptive behaviours, and has the potential to bring the physical abilities of robots one step closer to those of animals and humans.EPSR

Learning robot in-hand manipulation with tactile features

Dexterous manipulation enables repositioning of objects and tools within a robot’s hand. When applying dexterous manipulation to unknown objects, exact object models are not available. Instead of relying on models, compliance and tactile feedback can be exploited to adapt to unknown objects. However, compliant hands and tactile sensors add complexity and are themselves difficult to model. Hence, we propose acquiring in-hand manipulation skills through reinforcement learning, which does not require analytic dynamics or kinematics models. In this paper, we show that this approach successfully acquires a tactile manipulation skill using a passively compliant hand. Additionally, we show that the learned tactile skill generalizes to novel objects